Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12540/261
Title: Innovative and efficient method of robotics for helping the Parkinson's disease patient using IoT in big data analytics
Authors: Sivaparthipan, C.B. 
Muthu, Bala Anand 
Manogaran, Gunasekaran 
Maram, Balajee 
Sundarasekar, Revathi 
Krishnamoorthy, Sujatha 
Hsu, Ching‐Hsien 
Chandran, Karthik 
Issue Date: 2019
Publisher: John Wiley & Sons, Inc.
Source: Sivaparthipan, C. B., Muthu, B. A., Manogaran, G., Maram, B., Sundarasekar, R., Krishnamoorthy, S., Hsu, C., & Chandran, K. (2019). Innovative and efficient method of robotics for helping the parkinson's disease patient using iot in big data analytics. Transactions on Emerging Telecommunications Technologies, e3838.
Journal: Transactions on Emerging Telecommunications Technologies 
Abstract: Big data had accumulated a massive amount of stored data for applications including robotics, internet of things (IoT), and healthcare system. Although the IoT‐based healthcare system plays a vital role in big data industry, in some case, the sensing may be difficult to predict the accurate result. The proposed system with artificial intelligence and IoT for Parkinson's disease can enhance the gait performance tremendously. This research clearly defines the role of robots in Parkinson's disease and how they interact with big data analytics. To process the research scheme, data are collected from big data. Moreover, Laser scanned scheme with piecewise linear Gaussian dynamic time warp machine learning is introduced. In order to scan the path for obstacle and safe place, laser scan system is used. The main role of robot is to predict the walker motion and give physical training to the patient. To predict the walker motion of patient, robot has to walk along with patient since the sensors are fixed in both the patient and the robot. Finally, the performance of proposed methodology is evaluated with existing works.
Description: Please note that preprint copy is not available on WIRE. Please contact wire@wku.edu.cn to request an electronic copy of this item.
URI: https://hdl.handle.net/20.500.12540/261
DOI: 10.1002/ett.3838
Appears in Collections:Scholarly Publications

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